Version 1
: Received: 1 June 2023 / Approved: 5 June 2023 / Online: 5 June 2023 (05:15:33 CEST)
How to cite:
Iftimia, N.; Pandya, R.; Mahoney, F. New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making. Preprints2023, 2023060243. https://doi.org/10.20944/preprints202306.0243.v1
Iftimia, N.; Pandya, R.; Mahoney, F. New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making. Preprints 2023, 2023060243. https://doi.org/10.20944/preprints202306.0243.v1
Iftimia, N.; Pandya, R.; Mahoney, F. New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making. Preprints2023, 2023060243. https://doi.org/10.20944/preprints202306.0243.v1
APA Style
Iftimia, N., Pandya, R., & Mahoney, F. (2023). New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making. Preprints. https://doi.org/10.20944/preprints202306.0243.v1
Chicago/Turabian Style
Iftimia, N., Raj Pandya and Forest Mahoney. 2023 "New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making" Preprints. https://doi.org/10.20944/preprints202306.0243.v1
Abstract
(1) Background: Artificial intelligence (AI) has existed in some form for decades, but recent rapid advances in a subset called machine learning (ML) — and more specifically deep learning (DL), a neural network-based approach — have made headlines for the potential to revolutionize and automate multiple large sectors of society, including scientific research and the healthcare field. Furthermore, large language models (LLMs) — which are built on DL — could lead to a more seamless, natural interaction between humans and computers. (2) Methods: We reviewed numerous publications on this subject from recent years. (3) Results: We found these studies collectively show that AI is positively disrupting both biomedical research and medical practice, such as optical imaging in surgery guidance. (4) Conclusions: However, we recommend caution in over-reliance on AI in the laboratory or the clinic due to anticipated risks and current limitations.
Keywords
AI; artificial intelligence; automation; neural networks; machine learning; deep learning; large language models; biomedical research; imaging; healthcare
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
